Self-Supervised Multi-Scale Cropping and Simple Masked Attentive Predicting for Lung CT-Scan Anomaly Detection-Reference-Cited by-同舟云学术

Self-Supervised Multi-Scale Cropping and Simple Masked Attentive Predicting for Lung CT-Scan Anomaly Detection

Published:2024-01 Issue:1 Volume:43 Page:594-607
ISSN:0278-0062
Container-title:IEEE Transactions on Medical Imaging
language:
Short-container-title:IEEE Trans. Med. Imaging

Author:

Li Wei¹^ORCID,Liu Guang-Hai²^ORCID,Fan Haoyi³^ORCID,Li Zuoyong⁴^ORCID,Zhang David⁵^ORCID

Affiliation:

1. College of Computer Science and Mathematics, Fujian University of Technology, Fuzhou, China

2. College of Computer Science and Engineering, Guangxi Normal University, Guilin, China

3. School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China

4. Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Fujian Provincial Key Laboratory of Medical Big Data Engineering, College of Computer and Control Engineering, Minjiang University, Fuzhou, China

5. School of Data Science, The Chinese University of Hong Kong (Shenzhen), Shenzhen, China

Funder

National Natural Science Foundation of China

Open Project of Fujian Provincial Key Laboratory of Medical Big Data Engineering

Key Project of Colleges and Universities of Henan Province

Science and Technology Innovation 2030 “New Generation of Artificial Intelligence”

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Radiological and Ultrasound Technology,Software

Link

http://xplorestaging.ieee.org/ielx7/42/10379496/10246331.pdf?arnumber=10246331

Reference62 articles.

1. Diagnosis of Coronavirus Disease 2019 (COVID-19) With Structured Latent Multi-View Representation Learning

2. Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound

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4. A fully automated deep learning-based network for detecting COVID-19 from a new and large lung CT scan dataset

5. MAMA Net: Multi-Scale Attention Memory Autoencoder Network for Anomaly Detection